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$11

$59

$94

How much do computer system validation csv remote jobs pay per hour?

As of Aug 21, 2026, the average hourly pay for computer system validation csv remote in Concord, CA is $59.60, according to ZipRecruiter salary data. Most workers in this role earn between $45.91 and $70.67 per hour, depending on experience, location, and employer.

What is the difference between Computer System Validation Csv Remote vs Computer Validation Specialist?

AspectComputer System Validation Csv RemoteComputer Validation Specialist
CertificationsGxP, 21 CFR Part 11, CSV certificationsGxP, 21 CFR Part 11, CSV certifications
Work EnvironmentRemote, regulated industries like pharma and biotechOn-site or hybrid in regulated industries
Industry UsagePharmaceutical, biotech, medical devicesPharmaceutical, biotech, medical devices
Job FocusValidating computer systems and software complianceValidating computer systems and software compliance

Both roles require similar certifications and work in regulated industries like pharma and biotech. The main difference is that Computer System Validation Csv Remote typically involves remote work, focusing on validating computer systems remotely, while Computer Validation Specialist may work on-site or in hybrid settings. Both roles aim to ensure compliance with industry standards and regulations.

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Staff Machine Learning Engineer, Computer Vision

Pinterest

San Francisco, CA • On-site, Remote

Full-time

Re-posted 8 days ago

Job description

Within Pinterest, the Pinterest Labs organization focuses on applied ML research and development. Labs works across a broad variety of AI/ML initiatives-including core computer vision, multimodal representation learning, heterogeneous graph neural networks, generative modeling, and recommender systems. This is the group that develops the foundation ML models that fully leverage the tens of billions of Pins and the associated knowledge graph to improve the core product.

We are currently hiring for the Visual Modeling team in Labs, which develops Pinterest's in-house visual encoder. In this role, you'll work with Pinterest's rich visual-text dataset to train large-scale models from scratch that are continuously shipped to production to power visualization features. You'll build multimodal representations that power applications such as recommender systems, Semantic IDs, and a range of downstream ML models. The visual encoder also produces visual tokens that power our in-house VLM and composed image retrieval models. The core visual pod is a small group (~10 engineers) inside Labs, which allows for deep collaboration. For example, engineers working on multimodal representation also contribute to our internal text-to-image generation Canvas project-collaborating on autoencoder design or on reward function development for RL training.

What you'll do:

  • Prototype state-of-the-art visual encoders that power Pinterest's recommender systems and internal visual language models.
  • Experiment with billion-scale datasets and gain hands-on experience with large-scale GPU computing.
  • Build flexible visual reasoning tools such as composed image retrieval, promptable detection/segmentation, and instruction-tuned embedding and generative models.
  • Read research papers, participate in group discussions, and help brainstorm the company's overall visual generative strategy.
  • Help collect relevant visual instruction training data that can be shared across multimodal representation, composed image retrieval, text-to-image generation and visual language modeling.
  • Publish and share your work through conferences, paper submissions, and blog posts.
  • Mentor junior researchers and research interns within the Pinterest Labs organization.
  •  

What we're looking for:

  • Research engineers and scientists with experience building and training computer vision models.
  • Experience with multimodal representations and visual language modeling is strongly preferred.
  • A track record of research contributions (e.g., publications, open-source work) and/or shipping ML models to production.
  • Hands-on experience with large-scale model training and modern deep learning frameworks (e.g., PyTorch).
  • Strong collaboration skills and a demonstrated ability to work effectively in a small, fast-moving team.
  • M.S. or PhD in Machine Learning or related academic areas, or equivalent work experience.
  • Publications at top ML conferences
  • Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring

Relocation Statement:

  • This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

In-Office Requirement Statement:

  • We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
  • This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.

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